Hybrid quantum-classical algorithms for solving quantum chemistry in Hamiltonian–wave-function space

Yuan, Zhan-Hao; Yin, Tao; Zhang, Dan-Bo

doi:10.1103/physreva.103.012413

Cited by 12 publications

(6 citation statements)

References 40 publications

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“…Instead, we refer to these algorithms as "quantum inspired". To label our algorithm we followed the criterion used by other authors (O'Driscoll et al 2019;Yuan et al 2021). This is better suited to the fact that the hybrid quantum algorithms take advantage of the quantum mechanical properties of the states, while subrogating some calculations to classical algorithms.…”

Section: Introductionmentioning

confidence: 99%

Implementable hybrid quantum ant colony optimization algorithm

Andoin¹,

Osaba²,

Oregi³

et al. 2022

Quantum Mach. Intell.

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We propose a new hybrid quantum algorithm based on the classical Ant Colony Optimization algorithm to produce approximate solutions for NP-hard problems, in particular optimization problems. First, we discuss some previously proposed Quantum Ant Colony Optimization algorithms, and based on them, we develop an improved algorithm that can be truly implemented on near-term quantum computers. Our iterative algorithm codifies only the information about the pheromones and the exploration parameter in the quantum state, while subrogating the calculation of the numerical result to a classical computer. A new guided exploration strategy is used in order to take advantage of the quantum computation power and generate new possible solutions as a superposition of states. This approach is specially useful to solve constrained optimization problems, where we can implement efficiently the exploration of new paths without having to check the correspondence of a path to a solution before the measurement of the state. As an example of a NP-hard problem, we choose to solve the Quadratic Assignment Problem. The benchmarks made by simulating the noiseless quantum circuit and the experiments made on IBM quantum computers show the validity of the algorithm.

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Section: Introductionmentioning

confidence: 99%

Implementable hybrid quantum ant colony optimization algorithm

Andoin¹,

Osaba²,

Oregi³

et al. 2022

Quantum Mach. Intell.

View full text Add to dashboard Cite

show abstract

“…One such strong candidate is variational quantum algorithm (VQA) [2][3][4][5][6], which leverages up the expressive power of parameterized quantum circuits and a hybrid quantum-classical optimization procedure for finding solutions. Variational quantum algorithms have wide applications for solving quantum many-body systems [7,8], quantum chemistry [9][10][11][12][13], optimization problems [14][15][16][17], and so on. Among them, the quantum approximate optimization algorithm (QAOA) stands out as a VQA for tackling hard combinatorial optimization problems [18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A loop Quantum Approximate Optimization Algorithm with Hamiltonian updating

Duan¹

2021

Preprint

Self Cite

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Designing noisy-resilience quantum algorithms is indispensable for practical applications on Noisy Intermediate-Scale Quantum (NISQ) devices. Here we propose a quantum approximate optimization algorithm (QAOA) with a very shallow circuit, called loop-QAOA, to avoid issues of noises at intermediate depths, while still can be able to exploit the power of quantum computing. The key point is to use outputs of shallow-circuit QAOA as a bias to update the problem Hamiltonian that encodes the solution as the ground state. By iterating a loop between updating the problem Hamiltonian and optimizing the parameterized quantum circuit, the loop-QAOA can gradually transform the problem Hamiltonian to one easy for solving. We demonstrate the loop-QAOA on Max-Cut problems both with and without noises. Compared with the conventional QAOA whose performance will decrease due to noises, the performance of the loop-QAOA can still get better with an increase in the number of loops. The insight of exploiting outputs from shallow circuits as bias may be applied for other quantum algorithms.

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“…Instead, we refer to this algorithms as "quantum inspired". To label our algorithm we used the criteria used by other authors (O'Driscoll et al, 2019;Yuan et al, 2021). This criteria is better suited to the fact that the hybrid quantum algorithms take advantage of the quantum mechanical properties of the states, while subrogating some calculations to classical algorithms.…”

Section: Introductionmentioning

confidence: 99%

Implementable Hybrid Quantum Ant Colony Optimization Algorithm

de Andoin,

Echanobe

2021

Preprint

View full text Add to dashboard Cite

We propose a new hybrid quantum algorithm based on the classical Ant Colony Optimization algorithm to solve NP-hard problems, in particular optimization problems. First, we discuss some previously proposed Quantum Ant Colony Optimization algorithms, and based on them, we develop an improved algorithm that can be truly implemented on near-term quantum computers. Our iterative algorithm codes only the information about the pheromones and the exploration parameter in the quantum state, while subrogating the calculation of the numerical result to a classical computer. A new guided exploration strategy is used in order to take advantage of the quantum computation power and generate new possible solutions as a superposition of states. This approach is specially useful to solve constrained optimization problems, where we can implement efficiently the exploration of new paths using Fredkin gates without having to check the correspondence of a path to a solution before the measurement of the state. As an example of a NP-hard problem, we choose to solve the Quadratic Assignment Problem. The benchmarks made by simulating the noiseless quantum circuit and the experiments made on IBM quantum computers show the validity of the algorithm.

show abstract

Hybrid quantum-classical algorithms for solving quantum chemistry in Hamiltonian–wave-function space

Cited by 12 publications

References 40 publications

Implementable hybrid quantum ant colony optimization algorithm

Implementable hybrid quantum ant colony optimization algorithm

A loop Quantum Approximate Optimization Algorithm with Hamiltonian updating

Implementable Hybrid Quantum Ant Colony Optimization Algorithm

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